Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis

Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors all leaders in the statistics community introduce basic concepts from a data-analytic perspective before presenting advanced methods....

Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods.

There are many books covering survival analysis, but very few that cover the multivariate...

Intended for a second course in stationary processes, Stationary Stochastic Processes: Theory and Applications presents the theory behind the field's widely scattered applications in engineering and science. In addition, it reviews sample function properties and spectral representations for stationary processes and fields, including a portion on stationary point processes.

• Presents and illustrates the fundamental correlation and spectral methods for stochastic processes and random fields
• Explains how the basic theory is used...

A Balanced Treatment of Bayesian and Frequentist Inference

Statistical Inference: An Integrated Approach, Second Edition presents an account of the Bayesian and frequentist approaches to statistical inference. Now with an additional author, this second edition places a more balanced emphasis on both perspectives than the first edition.

New to the Second Edition

• New material on empirical Bayes and penalized likelihoods and their impact on regression models
• Expanded material on hypothesis testing,...

Designed for researchers and students, Nonlinear Times Series: Theory, Methods and Applications with R Examples familiarizes readers with the principles behind nonlinear time series models without overwhelming them with difficult mathematical developments. By focusing on basic principles and theory, the authors give readers the background required to craft their own stochastic models, numerical methods, and software. They will also be able to assess the advantages and disadvantages of different approaches, and thus be able to choose the right methods for their purposes.

Provides a Solid Foundation for Statistical Modeling and Inference and Demonstrates Its Breadth of Applicability

Stochastic Modeling and Mathematical Statistics: A Text for Statisticians and Quantitative Scientists addresses core issues in post-calculus probability and statistics in a way that is useful for statistics and mathematics majors as well as students in the quantitative sciences. The book s conversational tone, which provides the mathematical justification behind widely used statistical methods in a reader-friendly manner, and the book s many examples, tutorials, exercises and...

Unlike traditional books presenting stochastic processes in an academic way, this book includes concrete applications that students will find interesting such as gambling, finance, physics, signal processing, statistics, fractals, and biology. Written with an important illustrated guide in the beginning, it contains many illustrations, photos and pictures, along with several website links. Computational tools such as simulation and Monte Carlo methods are included as well as complete toolboxes for both traditional and new computational techniques.

This book introduces the theory of point processes and random objects. It begins by providing necessary background on inference, probability, measure theory, and stochastic processes. It then covers point process theory, including marked and spatio-temporal point processes. Lastly, the text discusses random objects and shape analysis. Written at the graduate level, this book presents the material in a concise and intuitive way, with many simulations, sample computations, and figures to illustrate the theory.